Systematic elucidation of independently modulated genes in Lactiplantibacillus plantarum reveals a trade‐off between secondary and primary metabolism

Abstract Lactiplantibacillus plantarum is a probiotic bacterium widely used in food and health industries, but its gene regulatory information is limited in existing databases, which impedes the research of its physiology and its applications. To obtain a better understanding of the transcriptional regulatory network of L. plantarum, independent component analysis of its transcriptomes was used to derive 45 sets of independently modulated genes (iModulons). Those iModulons were annotated for associated transcription factors and functional pathways, and active iModulons in response to different growth conditions were identified and characterized in detail. Eventually, the analysis of iModulon activities reveals a trade‐off between regulatory activities of secondary and primary metabolism in L. plantarum.


INTRODUCTION
The transcriptional regulatory network (TRN) of a bacterium consists of all regulatory interactions between its transcription factors (TFs) and genes (van Hijum et al., 2009).TFs, also referred to as sequence-specific DNA-binding factors, sense external signals and then bind to promoter regions of operons to regulate gene expression levels (Ishihama, 2012).To identify regulatory interactions between TFs and genes, the most commonly used experimental method is chromatin immunoprecipitation followed by sequencing (CHiP-seq) (Park, 2009).In CHiP-seq, antibodies are used to select TF proteins, and then DNA bound to TF proteins will be purified.DNA sequencing for the DNA-TF protein complex will determine the binding site on the genome.A group of genes with binding sites of the same TF are considered as a regulon.However, the drawbacks of CHiP-seq lie in its high cost, time-intensive nature and challenges in capturing the diverse growth conditions of bacteria (Kidder et al., 2011).
In recent years, many computational methods of in-silico reconstruction of TRN have been developed, such as coexpression network analysis (Lemoine et al., 2021) or supervised learning-based methods (e.g., GENIE3 (Huynh-Thu et al., 2010)).One of the most popular methods to reconstruct TRN is using independent component analysis (ICA) to decompose the gene expression matrix, which consists of transcriptomic data of different samples, into sets of independently modulated genes, called iModulons (IMs) (Sastry et al., 2019).Apart from derived IMs, ICA can also quantify IM activities in different samples.Unlike CHiP-seq being a 'bottom-up' method, ICA follows a 'top-down' approach.ICA has been extensively applied to study and improve the understanding of many bacteria's TRNs.For example, ICA of Vibrio natriegens transcriptomes unveils the genetic basis of its natural competency (Shin et al., 2023).ICA has also been used to discover therapeutic strategies for Streptococcus pyogenes by identifying carbon sources that control the expression of haemolytic toxins (Hirose et al., 2023).
Lactiplantibacillus plantarum is a gram-positive lactic acid bacterium that can be found in diverse ecological niches (Seddik et al., 2017).It has been widely used in food and health industries.For instance, it is the major bacterium involved in the fermentation of mozzarella cheese (De Angelis et al., 2008); L. plantarum-derived exopolysaccharides (EPSs) have various probiotic effects (Silva et al., 2019) and anticancer properties (Arasu et al., 2016).Due to the importance of L. plantarum in different biological processes, such as dairy product fermentation, its gene expression regulation has received interest in several studies.For example, Jung and Lee identified differentially expressed genes when L. plantarum was in the acidic condition (Jung & Lee, 2020).Unlike most studies focusing on single regulatory genes, Wels et al. reconstructed the gene regulatory network of L. plantarum on the basis of correlations between gene expression levels and conserved regulatory motifs (Wels et al., 2011).Nonetheless, the regulon information of L. plantarum in RegPrecise (Novichkov et al., 2013) only recorded 47 regulons and 210 TF binding sites, in contrast to 624 and 943 TF binding sites recorded for Bacillus subtilis and Escherichia coli, respectively.The lack of gene regulatory information hinders the study of L. plantarum's physiology and rational engineering of its cellular pathways.
Considering the value of L. plantarum in industry and research as well as the limited understanding of its TRN, this study managed to infer undiscovered regulatory interactions using ICA decomposition of the gene expression matrix and to further investigate how L. plantarum respond to different growth conditions (e.g., acid stress).Moreover, this study, through the analysis of IM activities, explored the growth strategy of L. plantarum, in terms of how it balances different biological processes (e.g., energy generation, carbohydrate metabolism, stress responses).

Data acquisition and preprocessing
The transcriptomic data used in the study were obtained from 4 independent studies that included various experimental conditions: response to pH decrease from 6.2 to 5.0 (Jung & Lee, 2020), treatment with N-3oxododecanoyl homoserine lactone (a quorum sensing molecule) (Spangler et al., 2019), contrasting habitats (e.g., bee extract) (Filannino et al., 2018) and change of carbon sources (Özcan et al., 2021).The metadata of sample conditions can be found in Table S1.In the data from the selected 4 studies, genes were all annotated based on the genome assembly of L. plantarum WCFS1 (ASM20385v3) (Siezen et al., 2012).All transcriptomic sequencing reads were normalized as RPKM (Reads Per Kilobase Million).Then, all samples were merged as a compendium of transcriptomic data (100 samples and 3000 genes).Before independent component analysis (ICA) was undertaken, the merged dataset was first log-transformed and then centred by subtracting the expression levels of the reference condition (i.e., wt_pH6.2 in Table S1).The data quality was demonstrated by the higher Pearson correlation coefficients (PCCs) between replicates than PCCs between non-replicates (Rychel et al., 2021) (Figure 1A).

Determination of iModulons
ICA decomposition of the merged dataset (i.e., the expression matrix, 100 samples and 3000 genes) was conducted using scripts in precise-db (https:// github.com/ SBRG/ preci se-db) (Rychel et al., 2020).The FastICA algorithm in   (Pedregosa et al., 2012) was used to calculate independent components with 100 iterations with a tolerance of 10 −7 , log(cosh(x)) as the contrast function, and parallel search algorithm.The OptICA method was used to determine the optimal number of independent components (McConn et al., 2021).The outputs of ICA were the iModulon matrix (M matrix, 3000 genes and 53 IMs) and Activity matrix (A matrix, 53 IMs and 100 samples) (Figure 1B).The M and A matrices can be found in https:// github.com/ Sizhe Qiu/ LPiMo dulons/ tree/ main/ data/ IMdata.
Gene weights in each column (for the corresponding IM) of the M matrix were used to determine each gene's IM membership.The threshold of gene weight absolute values for each IM was computed based on D'Agostino's K 2 test using the PyModulon package (https:// github.com/ SBRG/ pymod ulon) (Sastry et al., 2019).The default K 2 -statistic cut-off of 550 was used.The genes with weight absolute values above the threshold were the member genes of the IM.Before annotation, Ims were labelled as IM-1 to 53.

Annotation of iModulons via regulon enrichment analysis
Regulons of L. plantarum WCFS1 were obtained from RegPrecise (Novichkov et al., 2013).Ims that overlap with regulons were annotated via regulon enrichment analysis.The set of genes in each IM was compared to each regulon using the two-sided Fisher's exact test (False Discovery Rate (FDR) < 10 −5 ) (Sastry et al., 2019).After regulon enrichments were computed for Ims, regulatory annotations were manually determined based on the Venn diagrams of Ims and regulons (see Figure S1).In addition to Ims associated with only one regulon (e.g., PyrR IM (IM-36)), there were two different annotation expressions for combined regulon enrichments: intersection (+) and union (/).If a specific combinatorial regulation (genes controlled by multiple regulators) was observed in the Venn diagram of the IM and enriched regulons, then the IM was annotated with regulators linked by '+' (e.g., MalR+MdxR IM (IM-47)).Otherwise, '/' was used (e.g., ArgR/MleR IM (IM-26)).

Annotation of iModulons via motif comparison
Ims that do not overlap with known regulons were annotated via motif discovery and motif comparison.If a coding gene's 200 bp upstream region does not overlap with another gene (Taboada et al., 2010) and BDGP Neural Network Promoter Prediction (Reese, 2001) predicted this region to be a possible promoter (probability score > 0.8), then this 200 bp upstream region was used to search for sequence motifs using MEME (Bailey, 1994).Motif comparison by TOMTOM (Gupta et al., 2007) then determined the most possible TF based on the similarity of found motifs and TF binding site motifs in databases (e.g., RegTransBase (Cipriano et al., 2013)).The p-value and E-value thresholds set in TOMTOM were 0.05 and 10.To further validate whether genes in the IM are regulated by the found TF, PCCs of the expression levels of the TF gene and IM genes were computed.If the gene had significant correlations (p-value < 0.05) with most genes in the IM, then the TF would be used to annotate the IM.

Regulatory and functional annotations of identified iModulons
The derived 53 Ims account for 85% explained variance of the gene expression matrix.In each IM, genes with absolute values of weights higher than the threshold are determined as IM member genes (see Methods, Section "Determination of iModulons").The details of IM member genes can be found in https:// github.com/ Sizhe Qiu/ LPiMo dulons/ tree/ main/ data/ IMdata/ as IM_genes.csv.Among 53 Ims, 45 are nonempty and most Ims' sizes are within 20 (Figure 2A).Only 17% IM member genes overlap with genes in known regulons (Figure 2B), and hence, only 13 IMs could be annotated via regulon enrichment (Figure 2C).The details of regulatory annotations can be found in https:// github.com/ Sizhe Qiu/ LPiMo dulons/ blob/ main/ data/ IMdata/ IM_ annot ation.csv.
For the 13 IMs annotated with enriched regulons, most of them have either high recall or high precision (cutoff = 0.6) (Figure 2D).Venn diagrams showing regulon enrichments in IMs are provided in Figure S1.High recall means that the overlap (of IM and regulon) has high coverage of the regulon, while high precision means that the overlap has high coverage of the IM.IMs with low recall and low precision are considered to be incompletely matched with regulons, but that does not necessarily mean the IM's regulatory annotation is inaccurate.For example, the remaining 3 genes in CopR IM that are not included by the current CopR regulon of L. plantarum WCFS1 are lp_3055(copA), lp_3057(copper-binding protein) and lp_3058(copperbinding protein), but they are included by the CopR regulon of other closely related lactic acid bacteria (e.g., Lactococcus lactis subsp.lactis Il1403) (Magnani et al., 2008).Therefore, the low recall and precision are sometimes resulted by the incompleteness of currently known regulons.
In addition to IMs associated with regulons, there are 11 IMs annotated via motif search and comparison (Figures 2C and S2).Two representative examples are NagC IM (IM-8) and McbR IM (IM-31) (Figure 2E).Their regulatory annotations are validated by significant correlations between expression levels of TF genes and IM activities (Figure 2F).The remaining 21 IMs (Figure 2C) cannot be annotated via motif search and comparison either because the IM does not contain multiple possible promoter sequences for motif search (e.g.,  or TOMTOM (Methods, Section "Annotation of iModulons via Motif Comparison") fails to find a TF binding site motif with a high similarity to the found motif (e.g., IM-6).
IMs were also annotated with enriched functional pathways (see SI, 3.1), and the details of functional annotations can be found in https:// github.com/ Sizhe Qiu/ LPiMo dulons/ blob/ main/ data/ IMdata/ IM_ annot ation.csv.Apart from the uncharacterized group, 3 dominant functions of derived IMs are carbohydrate metabolism, prophage proteins and transcription (Figure 2G).Fur/LexA IM (IM-1) was functionally annotated as 'Stress', as LexA has already been found as a TF for stress response (Ravcheev et al., 2013).IM-19 was annotated as 'Translation', because genes in IM-19 were all ribosomal genes (e.g., rplV (lp_1039), large ribosomal subunit protein uL22).12% (scaled with IM sizes) of IMs are uncharacterized in functional annotation due to the lack of enriched functional pathways.

Comparison between iModulons and regulons
The difference between IMs and regulons can provide undiscovered regulatory information.Regulon enrichments of some IMs show combinatorial regulations of multiple TFs, such as MalR+MdxR IM.Based on the genomic organization, 6 genes in the region between 151,222 and 158,185 bp belong to the same operon (Figure 3A).While mdxE (lp_0175), mdxG (lp_0177) and lp_0178 are already included by both MalR and MdxR regulons, MalR+MdxR IM also captures the combinatorial regulatory signals for malS (lp_0179) and msmX (lp_0180), which share the same promoter with genes in the overlap of MalR and MdxR regulons.All genes in MalR+MdxR IM are involved in maltose/maltodextrin metabolism, which is the biological process regulated by MalR and MdxR (Muscariello et al., 2011;Ravcheev et al., 2013).
IMs also have the ability to identify genes with strong regulatory interactions with TFs from known regulons.For example, the Pearson correlation coefficients (PCCs) between TF genes and genes in the overlap (of the IM and regulon) exhibit higher distributions compared to those of genes in the regulon for ArgR and CcpA (Figure 3B,C).Nevertheless, the overlap does not always show stronger regulatory interactions.For example, genes in PyrR IM do not have significantly higher PCCs with the PyrR gene than with the genes in PyrR regulon (Figure 3D).

Active iModulons in response to different growth conditions
In addition to the M matrix, the A matrix is another output of ICA decomposition, which reveals IM activities of L. plantarum under different growth conditions.In response to acid stress (in terms of pH decrease), 4 active IMs are observed: Fur/LexA IM, CopR IM, McbR IM and PyrR IM (Figure 4A).IM activities of all 4 active IMs identified increase with the decrease of pH (Figure 4B-E).The gene expression levels of Fur (lp_3247) and LexA (lp_2063) both decrease with the decrease of pH, though the trends over three pH values are not consistently decreasing (Figure 4F).To further characterize acid-active IMs, regulatory networks are reconstructed as weighted correlation networks, and genomic organizations of genes in those IMs are further investigated.Fur/LexA IM, based on gene locations and the weighted correlation network, appear to contain two operons regulated by Fur and LexA separately: lp_0302 and lp_0304 regulated by Fur; lp_2809 and lp_2810 regulated by LexA (Figure 5A,B).The correlations between Fur and lp_0302, lp_0304 and lp_3014 are all negative, consistent with the previous finding that Fur is a repressor (Bagg & Neilands, 1987) (Figure 5A).The correlations between LexA and its regulated genes (i.e., lp_2809, lp_2810 and lp_3050) are positive, indicating that LexA functions as an activator to those genes (Figure 5A).For CopR, McbR and PyrR IMs, the correlations between TFs and regulated genes are all positive, suggesting that associated TFs all function as activators (Figure 5C,E,G).Unlike Fur/ LexA IM, member genes of those three IMs are mainly in single operons (Figure 5D,F,H).
On the other hand, the change of carbon sources can result in transcriptional regulations of carbohydrate metabolism (Deutscher, 2008), where GntR IM (IM-16) was found to be the most active IM in this study (Figure 6A).Genes in GntR IM mainly encode for the utilization of different carbon sources (e.g., pts9C (lp_0576), uptake of mannose; panD (lp_0579), aspartate 1-decarboxylase) and the biosynthesis of capsular polysaccharide (CPS) in the cell wall (e.g., cps1F (lp_1182), CPS biosynthesis protein CpsC).The biosynthesis of CPS is a part of primary metabolism (cellular biomass formation), different from that of EPS, belonging to secondary metabolism (Whitfield et al., 2020).GntR IM is annotated via motif comparison (Figure S2) due to the lack of regulon information, and hence, it is hard to determine which TF in the GntR family regulate genes in this IM.Top 4 GntR TF genes with highest PCCs with activities of GntR IM are lp_2615, lp_2651, lp_3633 and lp_0563 (Figure 6B).The PCCs between TF genes and genes in GntR IM show that lp_2615 and lp_0563 have significant negative correlations with genes in GntR IM, while lp_2651 and lp_3633 have significant positive correlations with genes in GntR IM (Figure 6C), which are consistent with the PCCs (Figure 6B).Possibly, genes in GntR IM are regulated by multiple GntR family TFs.

The trade-off between primary and secondary metabolism revealed by iModulon activities
Member genes of IMs derived in this study encode connected reactions in one or several metabolic pathways, and those reactions were visualized as networks (see SI, 3.2) to investigate the links between IMs and cellular metabolism (Figure 7).For acid-active IMs identified in Section "Active iModulons in Response to Different Growth Conditions", genes in McbR IM and PyrR IM encode for the biosynthesis of l-cysteine and uridine monophosphate, respectively (Figure 7A,B).EPS biosynthetic reactions encoded by genes in Fur/LexA IM and copper homeostasis encoded by genes in CopR IM are currently not included by model iBT721.
Next, 4 representative IMs, namely ArgR/MleR IM, CcpA IM, GntR IM and GalR/AraR IM, functionally annotated for amino acid metabolism, energy metabolism and carbohydrate metabolism, are selected to reconstruct metabolic pathways encoded by their member genes (Figure 2G).ArgR/MleR IM member genes encode for the biosynthesis of N-Acetyl-l-glutamate 5-semialdehyde from l-glutamine (Figure 7C).CcpA IM, as an IM for energy metabolism, contains a part of glycolysis, the conversion of glycerol to dihydroxyacetone phosphate and phosphorylation of nucleosides (Figure 7D).GntR IM member genes mainly encode for CPS biosynthesis, from the activation of monosaccharides to the polymerization as explained in Section "Active iModulons in Response to Different Growth Conditions" (Figure 7E).Two important carbohydrate metabolic pathways, namely galactose metabolism and pentose phosphate pathway are contained by GalR/ AraR IM (Figure 7F).
In contrast to Fur/LexA IM controlling secondary metabolism (EPS biosynthesis induced by acid stress) as shown in Section "Active iModulons in Response to Different Growth Conditions", ArgR/MleR, CcpA, GntR and GalR/AraR IMs (metabolic pathways visualized in Figure 7C-F) regulate primary metabolism.investigate the relationship between regulatory activities of two branches of cellular metabolism, PCCs were computed for the activities of Fur/LexA IM and 4 IMs for primary metabolism (Figure 8A-D).Significant inverse correlations between the activity of Fur/LexA IM and activities of ArgR/MleR IM, CcpA IM, GntR IM and GalR/AraR IM can be observed, suggesting a trade-off between the regulatory activities of secondary and primary metabolisms.Lactiplantibacillus plantarum in acidic media (e.g., bee extract (pH 4.7), tomato juice (pH 3.5), see Table S1) have higher Fur/ LexA IM activities and lower IM activities of the 4 IMs for primary metabolism than those in relatively neutral media (e.g., faecal extract (pH 5.9), see Table S1).Therefore, the balance between regulations of EPS biosynthesis and primary metabolism in L. plantarum appears to significantly depend on the acidity of extracellular environments.
To assess whether a trade-off relationship also exists between gene expression levels (in addition to regulatory activities) of secondary and primary metabolism, PCCs were computed between the total expression levels of genes in Fur/LexA IM (EPS biosynthetic genes) and (i) all glycolytic genes (central carbon catabolism) (Figure 8E) and (ii) genes in Translational IM (IM-19, ribosomal genes) (Figure 8F).An inverse correlation between gene expression levels of EPS biosynthetic genes and glycolytic genes is also observed (Figure 8E), though the correlation is not statistically significant.For EPS biosynthetic genes versus ribosomal genes, there is no inverse correlation between them (Figure 8F).

DISCUSSION
ICA decomposition of L. plantarum allowed us to identify 45 nonempty IMs, 53.3% of which were annotated with associated TFs via either regulon enrichment analysis (13 IMs) or motif comparison (11 IMs).Annotated IMs revealed several regulatory interactions that have not been reported by known regulons of L. plantarum, for example, malS (lp_0179) and msmX (lp_0180) captured by MalR+MdxR IM (Section "Comparison between iModulons and Regulons"), which contributed to the reconstruction of a more complete TRN.Furthermore, the Activity matrix (A matrix) output by ICA decomposition showed the change of regulatory activities of TFs in response to different growth conditions (e.g., acid stress, carbon source switch), leading to the identification and characterization of relevant active IMs (Section "Active iModulons in Response to Different Growth Conditions").Lastly, the analysis of relationships between IM activities unveiled a trade-off between secondary metabolism (acid stress-induced EPS biosynthesis) and primary metabolism in L. plantarum (Section "The Trade-off between Primary and Secondary Metabolism Revealed by iModulon Activities"), which might shed light on evolutionarily beneficial growth strategies.
Though IMs derived in this study provided regulatory information for the reconstruction of the TRN of L. plantarum, the performance of ICA decomposition was limited by the size of the expression matrix, compared to other ICA-based studies of bacterial transcriptomes (e.g., ICA of Corynebacterium glutamicum collected 263 samples from 29 independent projects (Zhao et al., 2023)).Compared to well-studied organisms such as E. coli, the amount of existing transcriptomic data of L. plantarum on NCBI Gene Expression Omnibus (https:// www.ncbi.nlm.nih.gov/ geo/ ) (Edgar et al., 2002) is much smaller.Also, due to the lack of operon annotation in L. plantarum's genome, motif search for TF binding sites in this study used estimated promoter regions, which lowered the accuracy and might explain why some IMs were uncharacterized.It is also worth noting that the novel regulatory interactions shown by ICA are just 'predicted' instead of 'confirmed'.To obtain a more valid conclusion, CHiP-seq experiments are needed to confirm those findings in future studies.
With regard to the relationship between secondary and primary metabolism, theoretical models such as Grime's competitor-stress-ruderal triangle (Bruggeman et al., 2023;Grime, 1977), Synthetic Chemostat Model (Panikov, 2021) and regulatory proteome allocation model (Qiu et al., 2023) all adopted a resource allocation framework to capture the balance between two branches of cellular metabolism.Through the correlations between the activities of identified IMs, this study provided evidence to the theoretical models for secondary metabolism proposed in previous studies by showing the growth strategy of L. plantarum that adjusts regulatory F I G U R E 7 Metabolic pathways encoded by IM member genes.Reaction information (names, associated genes and IMs) can be found in Table S2.Reaction abbreviations are adopted from the BIGG database (http:// bigg.ucsd.edu/ ) (King et al., 2016).for different metabolic pathways to to external stress signals (Section "The Trade-off tween Primary and Secondary Metabolism Revealed by iModulon Activities").However, the curated data in this study could not support a significant trade-off relationship between gene expression levels of primary and secondary metabolism.More transcriptomic and proteomic profiling for L. plantarum under different growth conditions is needed to quantitatively study the balance between stress and cellular growth.
To conclude, this study provided the in-silico TRN reconstruction for L. plantarum in a top-down manner and unveiled its growth strategy to balance primary and secondary metabolism with IM activities, in spite

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I G U R E 1 ICA decomposes the compendium of transcriptomic data to 45 nonempty iModulons.(A) Quality check of transcriptomic data with PCCs.Blue: replicate correlations; Yellow: non-replicate correlations.(B) Schematic illustration of ICA applied to the gene expression matrix.

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I G U R E 2 Regulatory and functional pathway annotations of IMs.(A) The histogram of IM sizes, 45 out of 53 IMs are nonempty.(B) The Venn diagram of all IM genes and regulon genes.87 genes in IMs are contained in known regulons.(C) The pie chart of regulatory annotation status.Blue: regulon enrichment; Green: motif comparison; Grey: uncharacterized.(D) Recall and precision of IMs with matched regulons.(E) Motif comparison of IM-8 and IM-31.(F) The significant correlations between IM activities and gene expression levels of associated TFs identified via motif comparison for IM-8 and IM-31 (p-value < 0.05).(G) The pie chart and treemap of functional annotations of IMs, the size of each fraction is scaled with the IM size.

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I G U R E 4 Identification of active IMs under the acidic condition.(A) The heatmap of IM activities at pH 6.2, 5.5 and 5.0.(B) IM activities of Fur/LexA IM at different pH values.(C) IM activities of CopR IM at different pH values.(D) IM activities of McbR IM at different pH values.(E) IM activities of PyrR IM at different pH values.(F) The expression levels of Fur (lp_3247) and LexA (lp_2063) at different pH values.(G) The expression levels of CopR (lp_3365) at different pH values.(H) The expression levels of McbR (lp_2772) at different pH values.(I) The expression levels of PyrR (lp_2704) at different pH values.

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Characterization of genes in acidity-active IMs.(A) The weighted correlation network of Fur, LexA and genes in Fur/LexA IM (IM-1).(B) Gene weights and gene locations of Fur/LexA IM. (C) The weighted correlation network of CopR and genes in CopR IM (IM-15).(D) Gene weights and gene locations of CopR IM. (E) The weighted correlation network of McbR and genes in McbR IM (IM-31).(F) Gene weights and gene locations of McbR IM. (G) The weighted correlation network of PyrR and genes in PyrR IM (IM-36).(H) Genomic organization of genes in PyrR IM (IM-36).Orange: overlap of IM and regulon; Green: genes only in the regulon.Edge weights in weighted correlation networks are scaled to PCCs.Red: positive correlation; Blue: negative correlation; Orange node: the gene in the IM; Purple node: the TF gene.

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I G U R E 6 Identification of the most active IM in response to different carbon sources: GntR IM (IM-16).(A) The heatmap of IM activities with different carbon sources.FOS, fructooligosaccharides; GLC, glucose; HMO, human milk oligosaccharides; PAC1, proanthocyanidin fraction 1; PAC2, proanthocyanidin fraction 2; XG, xyloglucans.Detailed information can be found in Özcan et al. (2021).(B) The correlations between expression levels of 4 GntR family TF genes and GntR IM activities (p-value < 0.05).Red dashed line: linear fit.(C) The weighted correlation networks of 4 GntR family TF genes and genes in GntR IM (p-value < 0.05).Edge weights are scaled to PCCs.Red: positive correlation; Blue: negative correlation; Orange node: the gene in the IM; Purple node: the TF gene.